Keynote: The Last Mile Problem: Why AI W...
summary
Panel argues that AI cuts grunt work but fails the “last mile” of context, trust and constrained devices: examples include device and OS fragmentation blocking deployment, new open-weighted models runnable on one GPU lowering cost, and inconsistent outputs—78% developers use AI but only 40% trust it. Agents also cause real harm (an incident where AI agents deleted live records for thousands). Concrete takeaways: focus on product-spec ROI, monetize side projects, and log what AI cannot do each day.
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chapters (6)
- 0:00Welcome
- 1:01Last mile problem in AI
- 3:54Trust and adoption challenges
- 6:00Human roles vs AI automation
- 8:06Key takeaways
- 9:50Q&A on AI job impact
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